CD91_sarcoma_explore.Rmd
Teddy
06/09/2019
CD91 Sarcoma Exploration
This code downloads the TCGA Sarcoma datasets from the cBioportal and looks at expression data related to the CD91 gene. The data will be split apart in multiple ways to compare disease status and related metrics to the level of CD91 mRNA expression.
We are looking to extract and explore the data seen here, which has many other features and expressed genes to explore: 
Any acronyms or ambiguous abbreviations can be resolved by looking at the Swagger API for cBioPortal http://oncotree.mskcc.org/cdd/swagger-ui.html#/
Set up environment
Here I load the relevant packages and set environment variables. Specialty package cgdsr is loaded from the cBioportal website.
Download the datasets
Use the cBio package to download TCGA PanCancer Atlas sarcoma cancer data from here.
## getCancerStudies... OK
## getCaseLists (1/2) ... OK
## getCaseLists (2/2) ... OK
## getGeneticProfiles (1/2) ... OK
## getGeneticProfiles (2/2) ... OK
## getClinicalData (1/1) ... OK
## getProfileData (1/6) ... OK
## getProfileData (2/6) ... OK
## getProfileData (3/6) ... OK
## getProfileData (4/6) ... OK
## getProfileData (5/6) ... OK
## getProfileData (6/6) ... OKPull Sarcoma Samples
Use the downloaded data to find the TCGA sarcoma data and isolate the RNA-seq expression data for all samples with such data.
#' @get available genetic profiles strings
sarc_genetic_profiles_RNAseq <- getGeneticProfiles(mycgds, target_study_id)[2,1]
sarc_genetic_profiles_muts <- getGeneticProfiles(mycgds, target_study_id)[5,1]
#' @get data slices for a specified list of genes, genetic profile and case list
RNAseq_profile_data <- getProfileData.CGDS(x = mycgds,
genes = c('LRP1','CD3E','CD8A','FOXP3','NCAM1'),
geneticProfiles = sarc_genetic_profiles_RNAseq,
caseList = sarc_caselist_id)
# MUT_profile_data <- getProfileData.CGDS(x = mycgds,
# genes = c('LRP1'),
# geneticProfiles = sarc_genetic_profiles_muts,
# caseList = sarc_caselist_id)
#' @get clinical data for the target sarcoma case list
sarc_clinical_data <- getClinicalData.CGDS(x = mycgds, caseList = sarc_caselist_id)
#' @get available mutation data for the sarcoma cancer study
sarc_mutation_data <- getMutationData.CGDS(x = mycgds,
caseList = sarc_caselist_id,
geneticProfile = sarc_genetic_profiles_muts,
genes = 'LRP1')
Create combined set
We have pulled all of the expression data, mutation data, and clinical data for all out samples in the target sarcoma studies. Now we need to combine them into a master sheet. We also do some conversion of values.
#'[format the data]
#' @create case_id column and convert to data.table
RNAseq_profile_data$case_id <- row.names(x = RNAseq_profile_data)
RNAseq_profile_data <- data.table(RNAseq_profile_data)
setnames(x = RNAseq_profile_data, old = "LRP1", new = "LRP1_mrna_TPM")
setnames(x = RNAseq_profile_data, old = "CD3E", new = "CD3E_mrna_TPM")
setnames(x = RNAseq_profile_data, old = "CD8A", new = "CD8A_mrna_TPM")
setnames(x = RNAseq_profile_data, old = "FOXP3", new = "FOXP3_mrna_TPM")
setnames(x = RNAseq_profile_data, old = "NCAM1", new = "NCAM1_mrna_TPM")
#' @create case_id column and convert to data.table
# MUT_profile_data$case_id <- row.names(x = MUT_profile_data)
# MUT_profile_data <- data.table(MUT_profile_data)
# setnames(x = MUT_profile_data, old = "LRP1", new = "mutation")
#' [not needed]
#' @format data and remove non-important rows
sarc_clinical_data$case_id <- row.names(x = sarc_clinical_data)
sarc_clinical_data <- data.table(sarc_clinical_data)
sarc_clinical_data <- sarc_clinical_data[,.(case_id, AGE, ANEUPLOIDY_SCORE, CANCER_TYPE,
CANCER_TYPE_ACRONYM, CANCER_TYPE_DETAILED,
DAYS_LAST_FOLLOWUP, DFS_MONTHS, DFS_STATUS,
DSS_MONTHS, DSS_STATUS, ETHNICITY,
FRACTION_GENOME_ALTERED, MUTATION_COUNT,
NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT,
OS_MONTHS, OS_STATUS,
PFS_MONTHS, PFS_STATUS, RACE, SEX,
TUMOR_TISSUE_SITE, TUMOR_TYPE)]
#' @format the mutation data
sarc_mutation_data <- data.table(sarc_mutation_data)
sarc_mutation_data <- sarc_mutation_data[,.(case_id, mutation_type, amino_acid_change)]
#' [Merge the datasets into one]
#' @merge RNAseq and MUT profile
full_data <- merge(RNAseq_profile_data, sarc_mutation_data, by = "case_id", all.x = TRUE)
full_data <- merge(full_data, sarc_clinical_data, by = "case_id", all.x = TRUE)
#'[Transform the output into log2 transformed]
# full_data[, log2_CD3E := log(x = CD3E_mrna_TPM, base = 2)]
# full_data[, log2_CD8A := log(x = CD8A_mrna_TPM, base = 2)]
# full_data[, log2_FOXP3 := log(x = FOXP3_mrna_TPM, base = 2)]
# full_data[, log2_LRP1 := log(x = LRP1_mrna_TPM, base = 2)]
# full_data[, log2_NCAM1 := log(x = NCAM1_mrna_TPM, base = 2)]
#' @this will just be done at the plotting stage. having this stuff clogs up datatable with excess
#'[Melt the table long so it plots easily]
full_data <- melt(data = full_data, id.vars = c(1,7:30), variable.name = "gene", value.name = "expr_TPM")
full_data[, gene := gsub(pattern = "_mrna_TPM", replacement = "", x = gene)]
#'[Assign labels to samples based on their percentile of LRP1 expression]
#' @create column for just the LRP1 expression value (needed for quantile making)
full_data[gene == "LRP1", LRP1_ref := expr_TPM]
full_data[, LRP1_ref := mean(LRP1_ref, na.rm = T), by = case_id]
#' [assign quantiles based on desired division]
#' @median
full_data[, Median := factor(ntile(x = LRP1_ref, n = 2), levels = 2:1)]
#' @tertiles
full_data[, Tertile := factor(ntile(x = LRP1_ref, n = 3), levels = 3:1)]
#' @quartiles
# full_data[, Quartile := factor(ntile(x = LRP1_ref, n = 4), levels = 4:1)]
#' @pentiles
full_data[, Pentile := factor(ntile(x = LRP1_ref, n = 5), levels = 5:1)]
#' @deciles
# full_data[, Decile := factor(ntile(x = LRP1_ref, n = 10), levels = 10:1)]
#' @remove columns used for the sake of assignment
full_data[, LRP1_ref := NULL]
Expression analysis
Time to systematically visualize all aspects of data.
Overall Expression
This section shows the mRNA reads in transcripts per million for all the target genes. The faded lines link the same cases across groups. An interactive plot can be produced if requested.
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout#' [Create simplified plot dataset]
p_data_long <- full_data[,.(case_id, Median, Tertile, Pentile, gene, expr_TPM)]
p_data_long[, expr_TPM := log2(expr_TPM)]
p_data_wide <- dcast(data = p_data_long, formula = case_id+Median+Tertile+Pentile~gene, value.var = "expr_TPM")
#'[Gene expression scatter plot]
ggplot(data = p_data_long, mapping = aes(x = gene, y= expr_TPM)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group=case_id, color = case_id), alpha = 0.15, show.legend = F) +
#geom_dotplot(binaxis = 'y', stackdir = 'center', binwidth = 0.25) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle(expression(Sarcoma~Tumor~Gene~Expression~-~RNA~seq~V2~(log[2]))) +
theme(plot.title = element_text(hjust = 0.5))## Warning: Removed 10 rows containing missing values (geom_point).## Warning: Removed 10 rows containing missing values (geom_path).
# p <- ggplot(data = p_data_long, mapping = aes(x = gene, y= expr_TPM)) +
# geom_jitter(position = position_jitter(0.1), shape = 21) +
# geom_line(aes(group=case_id, color = case_id), alpha = 0.15, show.legend = F) +
# theme_bw() +
# xlab("Gene") +
# theme(plot.title = element_text(hjust = 0.5))
# ggplotly(p)
LRP1 and CD3E
This section of code examines the relationship between LRP1 expression and CD3E expression based on quantile divisons into: * Medians (2 quantiles) * Tertiles (3 quantiles) * Pentiles (5 quantiles) * more quantiles are easy to do if needed. The highest number in the quantile corresponds to the highest expression group (e.g. with pentiles, the 5th pentile is the top 20% of log2 fold expressing samples). For each quintile breakdown, two graphs are shown: * LEFT: expression amount by group * RIGHT: expression of CD3E vs LRP1 scatterplot, plus correlation The scatterplot is particularly important to demonstrate how different quantile groups may/may not result in different expression profiles of immune marker genes. It also provides to statistical backing for any claims made.
#' [cd3e data set isolation]
cd3e_long <- p_data_long[gene == "LRP1"| gene == "CD3E"]
cd3e_long <- cd3e_long[!is.na(expr_TPM)]
cd3e_long <- cd3e_long[expr_TPM != "NaN"]
cd3e_long <- cd3e_long[expr_TPM != "-Inf"]
cd3e_wide <- dcast(data = cd3e_long, formula = case_id+Median+Tertile+Pentile~gene, value.var = "expr_TPM")
#'[Gene Expression grouped by Medians]
a <- ggplot(data = cd3e_long,
mapping = aes(x = gene, y= expr_TPM, group = Median, color = Median)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Median), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. CD3E | Medians") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = cd3e_wide$CD3E, x = cd3e_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = cd3e_wide[Median == "1"]$CD3E, x = cd3e_wide[Median == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = cd3e_wide[Median == 2]$CD3E, x = cd3e_wide[Median == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
b <- ggplot(data = cd3e_wide,
mapping = aes(x = LRP1, y = CD3E)) +
geom_point(shape = 21, aes(group = Median, color = Median)) +
theme_bw() +
ylab("CD3 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Median), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13, y = 10, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13, y = 9.5, label = paste("p:", p_value)) +
annotate("text", x = 12, y = 4.5, label = paste("R:", cor_value_1)) +
annotate("text", x = 12, y = 4, label = paste("p:", p_value_1)) +
annotate("text", x = 16, y = 4.5, label = paste("R:", cor_value_2)) +
annotate("text", x = 16, y = 4, label = paste("p:", p_value_2))
grid.arrange(a, b, ncol = 2)## Warning: Removed 11 rows containing non-finite values (stat_smooth).
## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
#'[Gene Expression grouped by Tertiles ==============================================]
c <- ggplot(data = cd3e_long,
mapping = aes(x = gene, y= expr_TPM, group = Tertile, color = Tertile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Tertile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. CD3E | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = cd3e_wide$CD3E, x = cd3e_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = cd3e_wide[Tertile == "1"]$CD3E, x = cd3e_wide[Tertile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = cd3e_wide[Tertile == 2]$CD3E, x = cd3e_wide[Tertile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = cd3e_wide[Tertile == 3]$CD3E, x = cd3e_wide[Tertile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
d <- ggplot(data = cd3e_wide,
mapping = aes(x = LRP1, y = CD3E)) +
geom_point(shape = 21, aes(group = Tertile, color = Tertile)) +
theme_bw() +
ylab("CD3 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Tertile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13, y = 10, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13, y = 9.5, label = paste("p:", p_value)) +
annotate("text", x = 12, y = 4.5, label = paste("R:", cor_value_1)) +
annotate("text", x = 12, y = 4, label = paste("p:", p_value_1)) +
annotate("text", x = 14, y = 4.5, label = paste("R:", cor_value_2)) +
annotate("text", x = 14, y = 4, label = paste("p:", p_value_2)) +
annotate("text", x = 16, y = 4.5, label = paste("R:", cor_value_3)) +
annotate("text", x = 16, y = 4, label = paste("p:", p_value_3))
grid.arrange(c, d, ncol = 2)## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
#'[Gene Expression grouped by Pentiles ============================================]
e <- ggplot(data = cd3e_long,
mapping = aes(x = gene, y= expr_TPM, group = Pentile, color = Pentile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Pentile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. CD3E | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = cd3e_wide$CD3E, x = cd3e_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = cd3e_wide[Pentile == "1"]$CD3E, x = cd3e_wide[Pentile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = cd3e_wide[Pentile == 2]$CD3E, x = cd3e_wide[Pentile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = cd3e_wide[Pentile == 3]$CD3E, x = cd3e_wide[Pentile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
onj_4 <- cor.test(y = cd3e_wide[Pentile == 4]$CD3E, x = cd3e_wide[Pentile == 4]$LRP1)
p_value_4 <- round(onj_4$p.value, digits = 4)
cor_value_4 <- round(onj_4$estimate, digits = 4)
onj_5 <- cor.test(y = cd3e_wide[Pentile == 5]$CD3E, x = cd3e_wide[Pentile == 5]$LRP1)
p_value_5 <- round(onj_5$p.value, digits = 4)
cor_value_5 <- round(onj_5$estimate, digits = 4)
f <- ggplot(data = cd3e_wide,
mapping = aes(x = LRP1, y = CD3E)) +
geom_point(shape = 21, aes(group = Pentile, color = Pentile)) +
theme_bw() +
ylab("CD3 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Pentile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 10, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 9.5, label = paste("p:", p_value)) +
annotate("text", x = 11.3, y = 4.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.3, y = 4, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 2.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 2, label = paste("(2)p:", p_value_2)) +
annotate("text", x = 14, y = 4.5, label = paste("(3)R:", cor_value_3)) +
annotate("text", x = 14, y = 4, label = paste("(3)p:", p_value_3)) +
annotate("text", x = 15.2, y = 2.5, label = paste("(4)R:", cor_value_4)) +
annotate("text", x = 15.2, y = 2, label = paste("(4)p:", p_value_4)) +
annotate("text", x = 16.7, y = 4.5, label = paste("(5)R:", cor_value_5)) +
annotate("text", x = 16.7, y = 4, label = paste("(5)p:", p_value_5))
grid.arrange(e, f, ncol = 2)## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
LRP1 and CD8A
#' [cd3e data set isolation]
cd8a_long <- p_data_long[gene == "LRP1"| gene == "CD8A"]
cd8a_long <- cd8a_long[!is.na(expr_TPM)]
cd8a_long <- cd8a_long[expr_TPM != "NaN"]
cd8a_long <- cd8a_long[expr_TPM != "-Inf"]
cd8a_wide <- dcast(data = cd8a_long, formula = case_id+Median+Tertile+Pentile~gene, value.var = "expr_TPM")
#'[Gene Expression grouped by Medians]
a1 <- ggplot(data = cd8a_long,
mapping = aes(x = gene, y= expr_TPM, group = Median, color = Median)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Median), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. CD8A | Medians") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = cd8a_wide$CD8A, x = cd8a_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = cd8a_wide[Median == "1"]$CD8A, x = cd8a_wide[Median == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = cd8a_wide[Median == 2]$CD8A, x = cd8a_wide[Median == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
b1 <- ggplot(data = cd8a_wide,
mapping = aes(x = LRP1, y = CD8A)) +
geom_point(shape = 21, aes(group = Median, color = Median)) +
theme_bw() +
ylab("CD8A expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Median), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13, y = 10, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13, y = 9.5, label = paste("p:", p_value)) +
annotate("text", x = 12, y = 4.5, label = paste("R:", cor_value_1)) +
annotate("text", x = 12, y = 4, label = paste("p:", p_value_1)) +
annotate("text", x = 16, y = 4.5, label = paste("R:", cor_value_2)) +
annotate("text", x = 16, y = 4, label = paste("p:", p_value_2))
grid.arrange(a1, b1, ncol = 2)## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing missing values (geom_point).
#'[Gene Expression grouped by Tertiles ==============================================]
c1 <- ggplot(data = cd8a_long,
mapping = aes(x = gene, y= expr_TPM, group = Tertile, color = Tertile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Tertile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. CD8A | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = cd8a_wide$CD8A, x = cd8a_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = cd8a_wide[Tertile == "1"]$CD8A, x = cd8a_wide[Tertile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = cd8a_wide[Tertile == 2]$CD8A, x = cd8a_wide[Tertile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = cd8a_wide[Tertile == 3]$CD8A, x = cd8a_wide[Tertile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
d1 <- ggplot(data = cd8a_wide,
mapping = aes(x = LRP1, y = CD8A)) +
geom_point(shape = 21, aes(group = Tertile, color = Tertile)) +
theme_bw() +
ylab("CD8A expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Tertile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13, y = 10, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13, y = 9.5, label = paste("p:", p_value)) +
annotate("text", x = 12, y = 4.5, label = paste("R:", cor_value_1)) +
annotate("text", x = 12, y = 4, label = paste("p:", p_value_1)) +
annotate("text", x = 14, y = 4.5, label = paste("R:", cor_value_2)) +
annotate("text", x = 14, y = 4, label = paste("p:", p_value_2)) +
annotate("text", x = 16, y = 4.5, label = paste("R:", cor_value_3)) +
annotate("text", x = 16, y = 4, label = paste("p:", p_value_3))
grid.arrange(c1, d1, ncol = 2)## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing missing values (geom_point).
#'[Gene Expression grouped by Pentiles ============================================]
e1 <- ggplot(data = cd8a_long,
mapping = aes(x = gene, y= expr_TPM, group = Pentile, color = Pentile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Pentile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. CD8A | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = cd8a_wide$CD8A, x = cd8a_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = cd8a_wide[Pentile == "1"]$CD8A, x = cd8a_wide[Pentile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = cd8a_wide[Pentile == 2]$CD8A, x = cd8a_wide[Pentile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = cd8a_wide[Pentile == 3]$CD8A, x = cd8a_wide[Pentile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
onj_4 <- cor.test(y = cd8a_wide[Pentile == 4]$CD8A, x = cd8a_wide[Pentile == 4]$LRP1)
p_value_4 <- round(onj_4$p.value, digits = 4)
cor_value_4 <- round(onj_4$estimate, digits = 4)
onj_5 <- cor.test(y = cd8a_wide[Pentile == 5]$CD8A, x = cd8a_wide[Pentile == 5]$LRP1)
p_value_5 <- round(onj_5$p.value, digits = 4)
cor_value_5 <- round(onj_5$estimate, digits = 4)
f1 <- ggplot(data = cd8a_wide,
mapping = aes(x = LRP1, y = CD8A)) +
geom_point(shape = 21, aes(group = Pentile, color = Pentile)) +
theme_bw() +
ylab("CD8A expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Pentile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 10, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 9.5, label = paste("p:", p_value)) +
annotate("text", x = 11.3, y = 4.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.3, y = 4, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 2.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 2, label = paste("(2)p:", p_value_2)) +
annotate("text", x = 14, y = 4.5, label = paste("(3)R:", cor_value_3)) +
annotate("text", x = 14, y = 4, label = paste("(3)p:", p_value_3)) +
annotate("text", x = 15.2, y = 2.5, label = paste("(4)R:", cor_value_4)) +
annotate("text", x = 15.2, y = 2, label = paste("(4)p:", p_value_4)) +
annotate("text", x = 16.7, y = 4.5, label = paste("(5)R:", cor_value_5)) +
annotate("text", x = 16.7, y = 4, label = paste("(5)p:", p_value_5))
grid.arrange(e1, f1, ncol = 2)## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing missing values (geom_point).
LRP1 and FOXP3
#' [cd3e data set isolation]
foxp3_long <- p_data_long[gene == "LRP1"| gene == "FOXP3"]
foxp3_long <- foxp3_long[!is.na(expr_TPM)]
foxp3_long <- foxp3_long[expr_TPM != "NaN"]
foxp3_long <- foxp3_long[expr_TPM != "-Inf"]
foxp3_wide <- dcast(data = foxp3_long, formula = case_id+Median+Tertile+Pentile~gene, value.var = "expr_TPM")
#'[Gene Expression grouped by Medians]
a2 <- ggplot(data = foxp3_long,
mapping = aes(x = gene, y= expr_TPM, group = Median, color = Median)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Median), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. FOXP3 | Medians") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = foxp3_wide$FOXP3, x = foxp3_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = foxp3_wide[Median == "1"]$FOXP3, x = foxp3_wide[Median == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = foxp3_wide[Median == 2]$FOXP3, x = foxp3_wide[Median == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
b2 <- ggplot(data = foxp3_wide,
mapping = aes(x = LRP1, y = FOXP3)) +
geom_point(shape = 21, aes(group = Median, color = Median)) +
theme_bw() +
ylab("FOXP3 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Median), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 7, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 7.5, label = paste("p:", p_value)) +
annotate("text", x = 11.4, y = 2.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.4, y = 2, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 0.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 0, label = paste("(2)p:", p_value_2))
grid.arrange(a2, b2, ncol = 2)## Warning: Removed 4 rows containing non-finite values (stat_smooth).
## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
#'[Gene Expression grouped by Tertiles ==============================================]
c2 <- ggplot(data = foxp3_long,
mapping = aes(x = gene, y= expr_TPM, group = Tertile, color = Tertile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Tertile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. FOXP3 | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = foxp3_wide$FOXP3, x = foxp3_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = foxp3_wide[Tertile == "1"]$FOXP3, x = foxp3_wide[Tertile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = foxp3_wide[Tertile == 2]$FOXP3, x = foxp3_wide[Tertile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = foxp3_wide[Tertile == 3]$FOXP3, x = foxp3_wide[Tertile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
d2 <- ggplot(data = foxp3_wide,
mapping = aes(x = LRP1, y = FOXP3)) +
geom_point(shape = 21, aes(group = Tertile, color = Tertile)) +
theme_bw() +
ylab("FOXP3 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Tertile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 7, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 7.5, label = paste("p:", p_value)) +
annotate("text", x = 11.4, y = 2.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.4, y = 2, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 0.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 0, label = paste("(2)p:", p_value_2)) +
annotate("text", x = 14, y = 2.5, label = paste("(3)R:", cor_value_3)) +
annotate("text", x = 14, y = 2, label = paste("(3)p:", p_value_3))
grid.arrange(c2, d2, ncol = 2)## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
#'[Gene Expression grouped by Pentiles ============================================]
e2 <- ggplot(data = foxp3_long,
mapping = aes(x = gene, y= expr_TPM, group = Pentile, color = Pentile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Pentile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. FOXP3 | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = foxp3_wide$FOXP3, x = foxp3_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = foxp3_wide[Pentile == "1"]$FOXP3, x = foxp3_wide[Pentile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = foxp3_wide[Pentile == 2]$FOXP3, x = foxp3_wide[Pentile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = foxp3_wide[Pentile == 3]$FOXP3, x = foxp3_wide[Pentile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
onj_4 <- cor.test(y = foxp3_wide[Pentile == 4]$FOXP3, x = foxp3_wide[Pentile == 4]$LRP1)
p_value_4 <- round(onj_4$p.value, digits = 4)
cor_value_4 <- round(onj_4$estimate, digits = 4)
onj_5 <- cor.test(y = foxp3_wide[Pentile == 5]$FOXP3, x = foxp3_wide[Pentile == 5]$LRP1)
p_value_5 <- round(onj_5$p.value, digits = 4)
cor_value_5 <- round(onj_5$estimate, digits = 4)
f2 <- ggplot(data = foxp3_wide,
mapping = aes(x = LRP1, y = FOXP3)) +
geom_point(shape = 21, aes(group = Pentile, color = Pentile)) +
theme_bw() +
ylab("FOXP3 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Pentile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 7, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 7.5, label = paste("p:", p_value)) +
annotate("text", x = 11.4, y = 2.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.4, y = 2, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 0.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 0, label = paste("(2)p:", p_value_2)) +
annotate("text", x = 14, y = 2.5, label = paste("(3)R:", cor_value_3)) +
annotate("text", x = 14, y = 2, label = paste("(3)p:", p_value_3)) +
annotate("text", x = 15.2, y = 0.5, label = paste("(4)R:", cor_value_4)) +
annotate("text", x = 15.2, y = 0, label = paste("(4)p:", p_value_4)) +
annotate("text", x = 16.7, y = 2.5, label = paste("(5)R:", cor_value_5)) +
annotate("text", x = 16.7, y = 2, label = paste("(5)p:", p_value_5))
grid.arrange(e2, f2, ncol = 2)## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
LRP1 and NCAM
#' [cd3e data set isolation]
ncam1_long <- p_data_long[gene == "LRP1"| gene == "NCAM1"]
ncam1_long <- ncam1_long[!is.na(expr_TPM)]
ncam1_long <- ncam1_long[expr_TPM != "NaN"]
ncam1_long <- ncam1_long[expr_TPM != "-Inf"]
ncam1_wide <- dcast(data = ncam1_long, formula = case_id+Median+Tertile+Pentile~gene, value.var = "expr_TPM")
#'[Gene Expression grouped by Medians]
a3 <- ggplot(data = ncam1_long,
mapping = aes(x = gene, y= expr_TPM, group = Median, color = Median)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Median), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. NCAM1 | Medians") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = ncam1_wide$NCAM1, x = ncam1_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = ncam1_wide[Median == "1"]$NCAM1, x = ncam1_wide[Median == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = ncam1_wide[Median == 2]$NCAM1, x = ncam1_wide[Median == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
b3 <- ggplot(data = ncam1_wide,
mapping = aes(x = LRP1, y = NCAM1)) +
geom_point(shape = 21, aes(group = Median, color = Median)) +
theme_bw() +
ylab("NCAM1 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Median), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 12, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 12.5, label = paste("p:", p_value)) +
annotate("text", x = 11.4, y = 7.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.4, y = 7, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 5.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 5, label = paste("(2)p:", p_value_2))
grid.arrange(a3, b3, ncol = 2)## Warning: Removed 4 rows containing non-finite values (stat_smooth).
## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
#'[Gene Expression grouped by Tertiles ==============================================]
c3 <- ggplot(data = ncam1_long,
mapping = aes(x = gene, y= expr_TPM, group = Tertile, color = Tertile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Tertile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. NCAM1 | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = ncam1_wide$NCAM1, x = ncam1_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = ncam1_wide[Tertile == "1"]$NCAM1, x = ncam1_wide[Tertile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = ncam1_wide[Tertile == 2]$NCAM1, x = ncam1_wide[Tertile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = ncam1_wide[Tertile == 3]$NCAM1, x = ncam1_wide[Tertile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
d3 <- ggplot(data = ncam1_wide,
mapping = aes(x = LRP1, y = NCAM1)) +
geom_point(shape = 21, aes(group = Tertile, color = Tertile)) +
theme_bw() +
ylab("NCAM1 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Tertile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 12, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 12.5, label = paste("p:", p_value)) +
annotate("text", x = 11.4, y = 7.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.4, y = 7, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 5.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 5, label = paste("(2)p:", p_value_2)) +
annotate("text", x = 14, y = 7.5, label = paste("(3)R:", cor_value_3)) +
annotate("text", x = 14, y = 7, label = paste("(3)p:", p_value_3))
grid.arrange(c3, d3, ncol = 2)## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
#'[Gene Expression grouped by Pentiles ============================================]
e3 <- ggplot(data = ncam1_long,
mapping = aes(x = gene, y= expr_TPM, group = Pentile, color = Pentile)) +
geom_jitter(position = position_jitter(0.1), shape = 21) +
geom_line(aes(group = case_id, color = Pentile), alpha = 0.1) +
theme_bw() +
ylab(expression(log[2]~mRNA~(TPM))) +
xlab("Gene") +
ggtitle("LRP1 vs. NCAM1 | Tertiles") +
theme(plot.title = element_text(hjust = 0.5))
#' @Extract correlation value and p values for overall and for each quartile
onj <- cor.test(y = ncam1_wide$NCAM1, x = ncam1_wide$LRP1)
p_value <- round(onj$p.value, digits = 4)
cor_value <- round(onj$estimate, digits = 4)
onj_1 <- cor.test(y = ncam1_wide[Pentile == "1"]$NCAM1, x = ncam1_wide[Pentile == "1"]$LRP1)
p_value_1 <- round(onj_1$p.value, digits = 4)
cor_value_1 <- round(onj_1$estimate, digits = 4)
onj_2 <- cor.test(y = ncam1_wide[Pentile == 2]$NCAM1, x = ncam1_wide[Pentile == 2]$LRP1)
p_value_2 <- round(onj_2$p.value, digits = 4)
cor_value_2 <- round(onj_2$estimate, digits = 4)
onj_3 <- cor.test(y = ncam1_wide[Pentile == 3]$NCAM1, x = ncam1_wide[Pentile == 3]$LRP1)
p_value_3 <- round(onj_3$p.value, digits = 4)
cor_value_3 <- round(onj_3$estimate, digits = 4)
onj_4 <- cor.test(y = ncam1_wide[Pentile == 4]$NCAM1, x = ncam1_wide[Pentile == 4]$LRP1)
p_value_4 <- round(onj_4$p.value, digits = 4)
cor_value_4 <- round(onj_4$estimate, digits = 4)
onj_5 <- cor.test(y = ncam1_wide[Pentile == 5]$NCAM1, x = ncam1_wide[Pentile == 5]$LRP1)
p_value_5 <- round(onj_5$p.value, digits = 4)
cor_value_5 <- round(onj_5$estimate, digits = 4)
f3 <- ggplot(data = ncam1_wide,
mapping = aes(x = LRP1, y = NCAM1)) +
geom_point(shape = 21, aes(group = Pentile, color = Pentile)) +
theme_bw() +
ylab("NCAM1 expression") +
xlab("LRP1 expression") +
ggtitle("Expression by Expression Matrix") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method = "lm", alpha = 0.2, linetype = "dashed", se = F) +
geom_smooth(aes(group = Pentile), method = "lm", alpha = 0.2, linetype = "dashed", color = "red", se = F) +
annotate("text", x = 13.1, y = 12, label = paste("Overall R:", cor_value)) +
annotate("text", x = 13.1, y = 12.5, label = paste("p:", p_value)) +
annotate("text", x = 11.4, y = 7.5, label = paste("(1)R:", cor_value_1)) +
annotate("text", x = 11.4, y = 7, label = paste("(1)p:", p_value_1)) +
annotate("text", x = 13, y = 5.5, label = paste("(2)R:", cor_value_2)) +
annotate("text", x = 13, y = 5, label = paste("(2)p:", p_value_2)) +
annotate("text", x = 14, y = 7.5, label = paste("(3)R:", cor_value_3)) +
annotate("text", x = 14, y = 7, label = paste("(3)p:", p_value_3)) +
annotate("text", x = 15.2, y = 5.5, label = paste("(4)R:", cor_value_4)) +
annotate("text", x = 15.2, y = 5, label = paste("(4)p:", p_value_4)) +
annotate("text", x = 16.7, y = 7.5, label = paste("(5)R:", cor_value_5)) +
annotate("text", x = 16.7, y = 7, label = paste("(5)p:", p_value_5))
grid.arrange(e3, f3, ncol = 2)## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
Survival Analysis
This data comes with information regarding several types of survival and other time series data amenable to Kaplan Meier analysis. This section of code examines these data.
Extraneous Analysis
This is a feature rich dataset with a lot of extra data like sex data, tumor location data, etc. We can view the expression data by certain quantiles and see if associations between something like low expression and remission or something related.